Another perspective on label noise is via uncertainty characterization [1]}. The noisy label generation process is probabilistic and random. Naturally uncertainty characterization comes into play. From the notion of deep learning uncertainty, the noise in the labels can be considered a type of aleatoric uncertainty, a measurement of the intrinsic and irreducible uncertainty within the data. Within aleatoric uncertainty, homoscedastic uncertainty is constant across the input while heteroscedastic uncertainty is dependent on the input. Hence if the noise transition matrix is a uniform or symmetric one, then the label noise can be considered homoscedastic; if it is label-dependent, then the label noise can be considered heteroscedastic. In recent noise simulation schemes, label noise is applied on samples that are more likely to be mislabeled given by pre-learned model [2]}. We consider such type of noise as epistemic uncertainty, a term that describes uncertainty induced by models.
